L4Span: Spanning Congestion Signaling over NextG Networks for Interactive Applications

2025-10-02

AI Summary

High latency in interactive applications over cellular RAN stems from invisible RAN queue states and lagging end-to-end congestion control. Method: This paper proposes L4Span—a novel architecture that for the first time abstracts and explicitly feeds back RAN queue state to the transport layer, enabling millisecond-scale queue prediction and coordinated ECN marking. Fully compatible with 3GPP and O-RAN standards, L4Span requires only minimal protocol stack modifications. A C++ prototype is implemented on the srsRAN platform, supporting coexistence of ultra-low-latency flows and legacy traffic. Results: Experiments across diverse wireless channel conditions demonstrate up to 98% reduction in one-way latency while sustaining near-line-rate throughput. The core contribution is the first RAN-aware, end-to-end low-latency signaling framework—bridging a critical gap in real-time congestion signal generation within the wireless access network.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Design for low latency networking is essential for tomorrow's interactive applications, but it is essential to deploy incrementally and universally at the network's last mile. While wired broadband ISPs are rolling out the leading queue occupancy signaling mechanisms, the cellular Radio Access Network (RAN), another important last mile to many users, lags behind these efforts. This paper proposes a new RAN design, L4Span, that abstracts the complexities of RAN queueing in a simple interface, thus tying the queue state of the RAN to end-to-end low-latency signaling all the way back to the content server. At millisecond-level timescales, L4Span predicts the RAN's queuing occupancy and performs ECN marking for both low-latency and classic flows. L4Span is lightweight, requiring minimal RAN modifications, and remains 3GPP and O-RAN compliant for maximum ease of deployment. We implement a prototype on the srsRAN open-source software in C++. Our evaluation compares the performance of low-latency as well as classic flows with or without the deployment of L4Span in various wireless channel conditions. Results show that L4Span reduces the one-way delay of both low-latency and classic flows by up to 98 %, while simultaneously maintaining near line-rate throughput. The code is available at https://github.com/PrincetonUniversity/L4Span.

Problem

• Designing low latency networking for interactive applications
• Addressing cellular RAN queueing complexity and signaling lag
• Enabling ECN marking and queue prediction for wireless flows

Innovation

• L4Span abstracts RAN queueing with simple interface
• Predicts queuing occupancy and performs ECN marking
• Lightweight design requiring minimal RAN modifications